Construction external damage fault diagnosis method, device and equipment and storage medium

Abstract

The invention discloses a construction external damage fault diagnosis method, device and equipment and a storage medium. The method comprises the following steps: acquiring fault information, identifying the fault information through a fault identification model to obtain identification result information, and determining a fault type based on the identification result information and first environment monitoring information; when the fault type belongs to a non-lightning stroke fault, performing feature analysis on fault waveform information in the fault information to determine whether the fault type belongs to a metallic fault, and determining whether the fault type belongs to a non-natural disaster fault through second environment monitoring information; and when the fault type belongs to a metallic fault and a non-natural disaster fault, the probability that the fault is a construction external damage fault is determined based on the fault information and the second environment monitoring information according to a preset construction external damage fault correlation factor research and judgment rule, so that the probability that the construction external damage fault occurs can be accurately analyzed and determined, and the fault diagnosis accuracy is improved. And the accuracy and reliability of line fault diagnosis are improved.

Description

Technical Field

[0001] The present invention relates to the field of transmission line construction external failure fault diagnosis, and in particular to a construction external failure fault diagnosis method, device, equipment and storage medium. Background Art

[0002] As an important part of the power system, the operation status of the transmission line is directly related to the stability and reliability of the power grid. However, in the actual operation process, the transmission line will inevitably be affected by various external factors, leading to the occurrence of faults. Therefore, it is of great significance to conduct effective fault diagnosis of the transmission line.

[0003] In the current field of transmission line construction external failure diagnosis, the current state of technology presents a diversified development trend. Commonly used technical means include infrared thermal imaging, drone inspections, and intelligent sensor monitoring, which have played a positive role in improving the efficiency and accuracy of fault detection. However, in the face of increasingly complex power grid environments and diverse fault types, existing diagnostic technologies still face many challenges. First, some online monitoring devices currently do not fully cover the extraction and application of typical characteristic parameters for line construction external failures, and further exploration and exploration are still needed; second, there has been no unified and clear division of common factors that cause construction external failures, which can easily lead to a low accuracy rate in the analysis of construction external failures. Summary of the invention

[0004] The present invention provides a construction external failure fault diagnosis method, device, equipment and storage medium to solve the problem of low construction external failure fault diagnosis accuracy in the prior art.

[0005] In a first aspect, an embodiment of the present invention provides a construction external failure fault diagnosis method, comprising:

[0006] Acquire fault information, identify the fault information through a fault identification model to obtain identification result information, and determine the fault type based on the identification result information and the first environment monitoring information;

[0007] When the fault type belongs to a non-lightning fault, determining whether the fault type belongs to a metallic fault by performing feature analysis on the fault waveform information in the fault information, and determining whether the fault type belongs to a non-natural disaster fault by using the second environmental monitoring information;

[0008] When the fault type is a metallic fault or a non-natural disaster fault, the probability that the fault is a construction external failure is determined based on the fault information and the second environmental monitoring information and according to the preset construction external failure fault correlation factor analysis rules.

[0009] Optionally, the acquiring fault information and identifying the fault information by using a fault identification model to obtain identification result information includes:

[0010] When protection action information is detected, obtaining the fault information;

[0011] Based on the fault identification model, time-frequency domain features of the fault waveform information in the fault information are extracted and analyzed to determine the identification result information.

[0012] Optionally, the method further includes:

[0013] Build a fault identification model based on the SMOTE (Synthetic Minority Oversampling Technique) algorithm and the deep forest algorithm;

[0014] The initial fault identification model is trained by using a historical fault data set to obtain the trained fault identification model.

[0015] Optionally, the construction external failure correlation factor includes at least one of the following: visualization information, fault phase information, fault time and meteorological information, wherein the visualization information and the fault time are determined by the fault information, the fault phase information is determined by the fault waveform information, and the meteorological information is determined by the second environment monitoring information;

[0016] The preset construction external failure correlation factor analysis rules include: key factor analysis rules, coupling factor analysis rules and exclusion factor analysis rules.

[0017] Optionally, based on the fault information and the second environmental monitoring information, according to a preset construction external failure fault correlation factor analysis rule, determining the probability of construction external failure fault diagnosis, includes:

[0018] Based on the fault information and the second environment monitoring information, determining the construction external damage correlation factor information corresponding to the fault;

[0019] Determine whether the construction external failure correlation factor information complies with the key factor analysis rules;

[0020] If it is true, it is determined that the fault is a construction failure;

[0021] If not, based on the construction external failure correlation factor information and through the coupling factor analysis rule, determine the first probability value of the fault being a construction external failure fault;

[0022] Based on the construction external failure associated factor information, the first probability value is updated through the exclusion factor analysis rule to determine a second probability value that the fault is a construction external failure fault;

[0023] A probability value that the fault is a construction failure fault is determined based on the second probability value.

[0024] Optionally, determining the probability value of the fault being a construction failure based on the second probability value includes:

[0025] Based on the fault identification model, obtaining a third probability value that the fault is a construction failure;

[0026] Based on the third probability value and the second probability value, a probability value that the fault is a construction failure is determined.

[0027] In a second aspect, an embodiment of the present invention provides a construction external failure fault diagnosis device, comprising:

[0028] An acquisition module, used to acquire fault information, identify the fault information through a fault identification model to obtain identification result information, and determine the fault type based on the identification result information and the first environment monitoring information;

[0029] A judgment module, used for determining whether the fault type is a metallic fault by performing feature analysis on the fault waveform information in the fault information when the fault type is a non-lightning fault, and determining whether the fault type is a non-natural disaster fault by using the second environmental monitoring information;

[0030] The determination module is used to determine the probability that the fault is a construction external failure based on the fault information and the second environmental monitoring information and according to the preset construction external failure failure correlation factor analysis rules when the fault type is a metallic fault or a non-natural disaster fault.

[0031] Optionally, the determining module is specifically used to:

[0032] Based on the fault information and the second environment monitoring information, determining the construction external damage correlation factor information corresponding to the fault;

[0033] Determine whether the construction external failure correlation factor information complies with the key factor analysis rules;

[0034] If it is true, it is determined that the fault is a construction failure;

[0035] If not, based on the construction external failure correlation factor information, a first probability value of the fault being a construction external failure is determined through a coupling factor analysis rule;

[0036] Based on the construction external failure associated factor information, the first probability value is updated by excluding factor analysis rules to determine a second probability value that the fault is a construction external failure fault;

[0037] A probability value that the fault is a construction failure fault is determined based on the second probability value.

[0038] In a third aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0039] at least one processor; and a memory communicatively coupled to the at least one processor;

[0040] Among them, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the construction external failure fault diagnosis method described in any embodiment of the present invention.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the construction external failure fault diagnosis method described in any embodiment of the present invention when executed.

[0042] The technical solution of the embodiment of the present invention obtains fault information, identifies the fault information through a fault identification model to obtain identification result information, and determines the fault type based on the identification result information and the first environmental monitoring information; when the fault type belongs to a non-lightning strike fault, determines whether the fault type belongs to a metallic fault by performing feature analysis on the fault waveform information in the fault information, and determines whether the fault type belongs to a non-natural disaster fault through the second environmental monitoring information; when the fault type belongs to a metallic fault and a non-natural disaster fault, then based on the fault information and the second environmental monitoring information, according to the preset construction external failure fault correlation factor analysis rules, determines the probability that the fault is a construction external failure fault, solves the problem of low construction external failure fault diagnosis accuracy in the prior art, can accurately analyze and determine the probability of construction external failure fault occurrence, and improves the accuracy and reliability of line fault diagnosis.

[0043] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A flowchart of a construction external failure diagnosis method provided in Embodiment 1 of the present invention;

[0046] Figure 2 A flowchart of a construction external failure diagnosis method provided in Embodiment 2 of the present invention;

[0047] Figure 3 An overall flow chart of a construction external failure diagnosis method provided in Embodiment 2 of the present invention;

[0048] Figure 4 A schematic diagram of the structure of a construction external failure diagnosis device provided in Embodiment 3 of the present invention;

[0049] Figure 5 A schematic diagram of the structure of an electronic device that can be used to implement an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0051] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0052] Embodiment 1

[0053] Figure 1 This is a flowchart of a construction external failure fault diagnosis method provided in the first embodiment of the present invention. This embodiment can be applied to the case of diagnosing a transmission line fault. The method can be executed by a construction external failure fault diagnosis device. The construction external failure fault diagnosis device can be implemented in the form of hardware and / or software. The construction external failure fault diagnosis device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0054] S110, acquiring fault information, identifying the fault information through a fault identification model to obtain identification result information, and determining the fault type based on the identification result information and the first environment monitoring information.

[0055] Among them, fault information may refer to information generated when a transmission line fails, for example, a fault waveform generated when a transmission line fails, the time when the transmission line fails, etc. A fault identification model may refer to a model that determines the type of fault by analyzing and identifying the fault waveform. The identification result information may refer to the type of fault to which the fault belongs. Currently, transmission line fault types can be divided into two categories: lightning strikes and non-lightning strikes; among them, non-lightning strikes include: construction external damage faults, bird damage, forest fire faults, wind deviation faults, etc. The first environmental monitoring information may refer to lightning monitoring information.

[0056] Specifically, when a transmission line fails, the fault waveform, fault time, etc. of the fault can be obtained through the dispatching device. The fault waveform is input into the fault identification model for analysis and identification to determine the fault type to which the fault belongs. Determine whether the fault type is a lightning strike or a non-lightning strike based on the fault type output by the fault identification model and the lightning monitoring information; for example, if the fault type output by the fault identification model is a non-lightning strike, and the lightning monitoring information shows that there is no lightning, then it is determined that the fault type is 100% a non-lightning strike fault. If the fault type output by the fault identification model is a lightning strike, and the lightning monitoring information shows that there is lightning, then it is determined that the fault type is 100% a lightning strike fault. Ignore the situation where the fault type output by the fault identification model is inconsistent with the lightning monitoring information.

[0057] In this embodiment, by acquiring fault information, the fault waveform in the fault information is input into the fault identification model for identification, and the fault type to which the fault belongs is output. The fault type is determined to be a lightning strike or non-lightning strike based on the fault type output by the fault identification model and the lightning monitoring information, thereby effectively improving the accuracy and reliability of the output line fault diagnosis.

[0058] S120. When the fault type is a non-lightning fault, determine whether the fault type is a metallic fault by performing feature analysis on the fault waveform information in the fault information, and determine whether the fault type is a non-natural disaster fault by using the second environmental monitoring information.

[0059] Among them, the second environmental monitoring information may refer to meteorological monitoring information such as heavy rain, typhoon, and strong wind disasters.

[0060] Specifically, when it is determined that the fault type belongs to a non-lightning fault, the fault waveform information in the fault information can be feature extracted, for example, amplitude features, phase features, and frequency features; and then transient component analysis and traveling wave feature analysis can be performed. Finally, through the preset judgment rules, for example, if the voltage amplitude of the barrier phase is lower than a certain threshold, and the phase difference between the fault phase and the non-fault phase exceeds a certain angle, and the transient component attenuation time constant is less than a certain value, it can be preliminarily judged as a metallic fault. Alternatively, it can be directly determined whether the fault type belongs to a metallic fault through a pre-trained recognition model, for example, a fault identification model. If the second environmental monitoring information indicates that there is a rainstorm, typhoon, or strong wind disaster, it is determined that the fault type belongs to a natural disaster fault. If the second environmental monitoring information indicates that there is no rainstorm, typhoon, or strong wind disaster, it is determined that the fault type belongs to a non-natural disaster fault.

[0061] In this embodiment, by performing feature analysis on the fault waveform information in the fault information again, it can be determined whether the fault type is a metallic fault, and by determining whether the fault type is a non-natural disaster fault through the second environmental monitoring information, the accuracy and reliability of line fault diagnosis are further improved through precise analysis.

[0062] S130. When the fault type is a metallic fault or a non-natural disaster fault, based on the fault information and the second environmental monitoring information, according to the preset construction external failure fault correlation factor analysis rules, determine the probability that the fault is a construction external failure.

[0063] The preset construction external failure fault correlation factor analysis rule may refer to a weight calculation rule for the construction external failure fault type correlation factor set in advance based on historical operation and maintenance data. Further, the analysis rule may be dynamically adjusted based on the update of historical operation and maintenance data.

[0064] Specifically, more than 90 kinds of correlation factors related to the fault type are pre-sorted out, and a typical fault factor library is established for the construction external failure type, and the construction external failure correlation factor analysis rules are set based on historical operation and maintenance data. When the fault type belongs to a metallic fault or a non-natural disaster fault, the probability value of the fault being a construction external failure is determined by calculating different weight values ​​for the fault waveform, fault time, visualization information, and meteorological monitoring information according to the pre-set correlation factor analysis rules for the construction external failure type, so as to achieve accurate analysis and determine the probability of construction external failure, and effectively improve the accuracy and reliability of line fault diagnosis.

[0065] In this embodiment, by acquiring fault information, the fault information is identified through a fault identification model to obtain identification result information, and the fault type is determined based on the identification result information and the first environmental monitoring information; when the fault type belongs to a non-lightning fault, the fault waveform information in the fault information is characteristically analyzed to determine whether the fault type belongs to a metallic fault, and the second environmental monitoring information is used to determine whether the fault type belongs to a non-natural disaster fault; by sorting out and refining the construction external failure judgment factors, and setting construction external failure fault correlation factor judgment rules based on historical operation and maintenance data, when the fault type belongs to a metallic fault and a non-natural disaster fault, the fault information and the second environmental monitoring information can be calculated with different weight values ​​according to the preset construction external failure fault correlation factor judgment rules, and then the probability value of the fault being a construction external failure is obtained, which solves the problem of low construction external failure fault diagnosis accuracy in the prior art, can accurately analyze and determine the probability of construction external failure faults, and improve the accuracy and reliability of line fault diagnosis.

[0066] Optionally, the acquiring fault information and identifying the fault information by using a fault identification model to obtain identification result information includes:

[0067] When protection action information is detected, obtaining the fault information;

[0068] Based on the fault identification model, time-frequency domain features of the fault waveform information in the fault information are extracted and analyzed to determine the identification result information.

[0069] Among them, the protection action information may refer to the fault signal sent by the protection device; the time-frequency domain characteristics of the fault waveform may include: 16 time-domain features, 13 frequency-domain features, 6 EWF component information entropies, weighted energy entropy, EWF4 sample entropy and EWF1 root mean square value, totaling 38 features.

[0070] Specifically, when a transmission line fails, the protection device will send a fault signal to the dispatching device, and the dispatching device will receive the fault information of the fault. The fault waveform information in the fault information is input into the fault identification model, and the fault waveform is extracted in the time and frequency domain through the fault identification model, and a total of 38 characteristic values ​​including 16 time domain features, 13 frequency domain features, 6 EWF component information entropy, weighted energy entropy, EWF4 sample entropy and EWF1 root mean square value can be obtained; by comparing and analyzing each characteristic value with the historical fault data set, the fault type to which the fault belongs is determined.

[0071] In this embodiment, when protection action information is detected, fault information is received through the dispatching device; time-frequency domain feature extraction is performed on the fault waveform information in the fault information based on the fault identification model, and the extracted characteristic values ​​are compared and analyzed with the historical fault data set to determine the fault type to which the fault belongs. Through precise analysis, the accuracy and reliability of output line fault diagnosis are effectively improved.

[0072] Optionally, this embodiment may further include:

[0073] Construct an initial fault identification model based on the SMOTE algorithm and deep forest algorithm;

[0074] The initial fault identification model is trained by using a historical fault data set to obtain the trained fault identification model.

[0075] Among them, the SMOTE algorithm can refer to solving the problem of data category imbalance by synthesizing new minority class samples. The deep forest algorithm can refer to simulating the hierarchical feature extraction capability of deep learning, and realizing hierarchical feature extraction by building a multi-layer random forest structure. The historical fault data set can refer to the fault transient waveform data set established based on historical output line fault information.

[0076] Specifically, the time-frequency features of the voltage and current waveforms in the historical fault data set can be extracted, and the faults can be classified according to the extracted time-frequency features to form fault waveform data sets of different fault types: the unbalanced data set in the fault waveform data set can be clustered by a clustering algorithm, and the clustered unbalanced data set can be expanded by SMOTE sampling; a fault sub-data set can be formed according to the balanced data set and the expanded unbalanced data set: the fault sub-data set can be used to train the model based on the deep forest algorithm to obtain a trained fault identification model.

[0077] In this embodiment, an initial fault identification model is constructed based on the SMOTE algorithm and the deep forest algorithm; the initial fault identification model is trained by using a historical fault data set to obtain the trained fault identification model; the fault type is diagnosed by using the trained fault identification model, which has good accuracy and effectiveness, and provides high-accuracy and high-coverage diagnosis for transmission line faults.

[0078] Optionally, the construction external failure correlation factor includes at least one of the following: visualization information, fault phase information, fault time and meteorological information, wherein the visualization information and the fault time are determined by the fault information, the fault phase information is determined by the fault waveform information, and the meteorological information is determined by the second environment monitoring information;

[0079] The preset construction external failure correlation factor analysis rules include: key factor analysis rules, coupling factor analysis rules and exclusion factor analysis rules.

[0080] Among them, visualization information can refer to whether the information of construction machinery alarm is displayed in the visualization interface; fault phase information can refer to the information that the fault phase is the lower phase or the side phase; fault time can refer to the time when the construction external failure occurs, for example, 5:00-23:00; meteorological information can refer to rainstorm, typhoon and strong wind disaster information. Key factor judgment rules can refer to the judgment rules that can 100% determine the occurrence of construction external failure; coupling factor judgment rules can refer to the judgment rules that can determine the probability value of construction external failure, which is greater than 0% and less than 100%; exclusion factor judgment rules can refer to the judgment rules that exclude other faults, and other faults can refer to other faults except construction external failure.

[0081] Specifically, visualization information and fault time can be directly determined through fault information, fault phase information can be determined by analyzing the fault waveform in the fault information, and meteorological information can be directly determined through the second environment monitoring information. More than 90 types of related factors related to fault types are pre-sorted out, and a typical fault factor library is established for construction external failure types, and key factor judgment rules, coupling factor judgment rules, and exclusion factor judgment rules for construction external failures are set based on historical operation and maintenance data.

[0082] For example, the key factor analysis rule can be: visual construction machinery alarm + fault phase is lower phase or side phase + meteorological information does not have rainstorm, typhoon, strong wind disaster warning + fault time: 5:00-23:00 (100% certain that the fault is a construction external failure)

[0083] The coupling factor analysis rule can be:

[0084] Visualization shows that there is a construction machinery alarm + there is no rainstorm, typhoon, or strong wind disaster warning in the meteorological information + the fault phase is the lower phase / edge phase (the probability value of determining that the fault is a construction external failure is 80%);

[0085] Visualized construction machinery alarm + meteorological information without rainstorm, typhoon, strong wind disaster warning + fault time: 5:00-23:00 (the probability of determining that the fault is a construction external failure is 80%);

[0086] The fault phase is the lower phase or the side phase + there is no rainstorm, typhoon, or strong wind disaster warning in the meteorological information + the fault time is from 5:00 to 23:00 (the probability value of determining that the fault is a construction external failure is 50%);

[0087] Visualized construction machinery alarm + meteorological information shows no rainstorm, typhoon, or strong wind disaster warning (the probability of determining that the fault is a construction external failure is 50%);

[0088] Visualization includes construction machinery alarm (the probability of determining that the fault is a construction external failure is 40%), fault lower phase or side phase (the probability of determining that the fault is a construction external failure is 15%), fault time: 5:00-23:00 (the probability of determining that the fault is a construction external failure is 15%), hidden dangers at construction hidden danger points (the probability of determining that the fault is a construction external failure is 20%), power outage for maintenance of the line on the same tower (the probability of determining that the fault is a construction external failure is 30%), unsuccessful reclosing (the probability of determining that the fault is a construction external failure is 25%), and metallic grounding fault (the probability of determining that the fault is a construction external failure is 15%).

[0089] The rules for judging exclusion factors can be: disaster warning: typhoon, heavy rain, strong wind (-100%, that is, it is determined that the fault is definitely not a construction failure), phase-to-phase fault (-80%, that is, it is determined that 80% of the fault is not a construction failure), successful reclosing (-60%, that is, it is determined that 60% of the fault is not a construction failure), fault line is ±800kV or 1000kV (-10%, that is, it is determined that 10% of the fault is not a construction failure).

[0090] In this embodiment, the construction external failure correlation factor information corresponding to the fault is determined through the fault information and the second environment monitoring information; according to the preset construction external failure fault key factor analysis rules, coupling factor analysis rules and exclusion factor analysis rules, the probability of construction external failure fault diagnosis is determined, which has good accuracy and provides high-accuracy and high-efficiency diagnosis for transmission line faults.

[0091] Embodiment 2

[0092] Figure 2 This is a flow chart of a construction external failure diagnosis method provided by the second embodiment of the present invention. The technical solution of this embodiment is further refined on the basis of the above embodiment. Figure 2 As shown, the method includes:

[0093] S210, acquiring fault information, identifying the fault information through a fault identification model to obtain identification result information, and determining the fault type based on the identification result information and the first environment monitoring information.

[0094] S220. When the fault type is a non-lightning fault, determine whether the fault type is a metallic fault by performing feature analysis on the fault waveform information in the fault information, and determine whether the fault type is a non-natural disaster fault by using the second environmental monitoring information.

[0095] S230: Determine construction external damage correlation factor information corresponding to the fault based on the fault information and the second environment monitoring information.

[0096] The construction external failure correlation factor information may refer to information on correlation factors that affect the judgment of construction external failure.

[0097] Specifically, the construction external failure correlation factor information may include: fault waveform information, reclosing information, visualization information, meteorological information, fault phase information, terrain environment information, fault time and season, historical fault information, hidden danger conditions, etc. Part of the construction external failure correlation factor information can be determined by fault information and second environmental monitoring information, such as fault waveform information and meteorological information; part of the construction external failure correlation factor information can be manually entered, such as terrain environment information and hidden danger conditions.

[0098] In this embodiment, all construction external damage associated factor information corresponding to the fault can be determined through fault information, second environment monitoring information and manually entered information, which effectively improves the coverage of construction external damage associated factors and further improves the accuracy and reliability of line fault diagnosis.

[0099] S240: Determine whether the construction external failure correlation factor information complies with the key factor analysis rules.

[0100] Specifically, based on the visualization information, fault phase information, fault time and meteorological information, it is judged that it meets the visualization that there is a construction machinery alarm + the fault phase is the lower phase or the side phase + the meteorological information does not contain heavy rain, typhoon, and strong wind disaster warning + fault time: 5:00-23:00.

[0101] In this embodiment, by judging the construction external failure related factor information through the key factor analysis rules, it can be determined whether the fault is 100% a construction external failure fault, thereby improving the judgment coverage of the construction external failure related factors and improving the accuracy of the construction external failure fault diagnosis.

[0102] S250: If the conditions are met, determine that the fault is a construction external failure.

[0103] In this embodiment, if the construction external failure correlation factor information meets the key factor analysis rules, it is directly determined that the fault is 100% a construction external failure fault.

[0104] S260: If not, based on the construction external failure associated factor information and through the coupling factor analysis rule, determine a first probability value that the fault is a construction external failure fault.

[0105] The first probability value may refer to the probability determined by the coupling factor analysis rule.

[0106] In this embodiment, all construction external damage associated factor information corresponding to the fault can be determined through fault information, second environment monitoring information and manually entered information, and all construction external damage associated factor information corresponding to the fault can be judged through coupling factor judgment rules to determine the first probability value of the fault being a construction external damage fault, thereby improving the judgment coverage of construction external damage associated factors and improving the accuracy of construction external damage fault diagnosis.

[0107] S270. Based on the construction external failure associated factor information, the first probability value is updated through the exclusion factor analysis rule to determine a second probability value that the fault is a construction external failure.

[0108] The second probability value may refer to the updated first probability value.

[0109] Specifically, after determining the first probability value, the construction external failure related factor information can be further analyzed and judged through the exclusion factor analysis rule. If the construction external failure related factor information meets the exclusion factor analysis rule, the corresponding negative probability is determined. On the basis of the first probability value, the negative probability is superimposed, and the second probability value that the fault is a construction external failure is determined. Exemplarily, if the first probability value is 80%, and the negative probability obtained by the exclusion factor analysis rule is -60%, then the second probability value obtained is 20%. If the construction external failure related factor information does not meet the exclusion factor analysis rule, the corresponding negative probability is determined to be 0.

[0110] In this embodiment, after determining the first probability value, the first probability value is updated by eliminating factor analysis rules to determine the second probability value that the fault is a construction external failure. By accurately analyzing and determining the probability of a construction external failure, the accuracy and reliability of line fault diagnosis are effectively improved.

[0111] S280. Determine a probability value that the fault is a construction failure based on the second probability value.

[0112] In this embodiment, by acquiring fault information, the fault information is identified through a fault identification model to obtain identification result information, and the fault type is determined based on the identification result information and the first environmental monitoring information; when the fault type belongs to a non-lightning fault, the fault waveform information in the fault information is characteristically analyzed to determine whether the fault type belongs to a metallic fault, and the second environmental monitoring information is used to determine whether the fault type belongs to a non-natural disaster fault; by sorting out and refining the construction external failure judgment factors, and setting construction external failure fault correlation factor judgment rules based on historical operation and maintenance data, when the fault type belongs to a metallic fault and a non-natural disaster fault, the fault information and the second environmental monitoring information can be judged according to the preset construction external failure fault correlation factor judgment rules including: key factor judgment rules, coupling factor judgment rules and exclusion factor judgment rules, and then the probability value of the fault being a construction external failure fault is obtained, and the accuracy and reliability of line fault diagnosis are effectively improved by accurately analyzing and determining the probability of construction external failure faults.

[0113] Optionally, determining the probability value of the fault being a construction failure based on the second probability value includes:

[0114] Based on the fault identification model, obtaining a third probability value that the fault is a construction failure;

[0115] Based on the third probability value and the second probability value, a probability value that the fault is a construction failure is determined.

[0116] The third probability may refer to a probability value corresponding to the fault type output by the fault identification model after analyzing and identifying the fault waveform.

[0117] Specifically, the weights of the third probability value and the second probability value may be preset; for example, the weight ratio of the third probability value may be preset as α, the weight ratio of the third probability value may be preset as β, the third probability value may be A, the second probability value may be B, and the final probability value of the fault being a construction external failure is C=α×A+β×B, where α+β=1. For example, if α=50%, β=50%, A=70%, and B=80%, the final probability value of the fault being a construction external failure is 75%.

[0118] In this embodiment, based on the fault identification model, a third probability value that the fault is a construction external failure is obtained; by combining the third probability value and the second probability value, the probability value of the fault being a construction external failure is determined, thereby achieving accurate analysis and determination of the probability of a construction external failure, and effectively improving the accuracy and reliability of line fault diagnosis.

[0119] For example, Figure 3The overall flow chart of a construction external failure diagnosis method provided by the second embodiment of the present invention is as follows: Figure 3 As shown in the figure, the specific steps for diagnosing construction external failures include:

[0120] 1. When protection action information is detected.

[0121] 2. Obtain distributed fault information (i.e., fault information).

[0122] 3. Comprehensively determine whether the fault is a non-lightning fault by combining the distributed waveform identification result information (ie, the identification result information) and the lightning monitoring information (ie, the first environment monitoring information).

[0123] 4. If the fault type is non-lightning fault, the distributed / recorded waveform (fault waveform information) characteristics are analyzed to determine whether the fault is a metallic fault, and the meteorological disaster information (i.e., the second environment monitoring information) is used to determine whether the fault is a non-natural disaster fault.

[0124] 5. If the fault type is a metallic fault or a non-natural disaster fault, the construction external damage correlation factor information corresponding to the fault is determined through the fault information and the second environment monitoring information.

[0125] 6. Determine whether the construction external failure related factor information complies with the key factor analysis rules; if so, determine that the fault is a construction external failure.

[0126] 7. If it does not meet the requirements, based on the construction external failure regularity factor (i.e. the fault information and the construction external failure correlation factor information corresponding to the fault determined by the second environmental monitoring information), and the manually entered fault factor (i.e. the manually entered construction external failure correlation factor information), the first probability value of the fault being a construction external failure is determined through the coupling factor analysis rules.

[0127] 8. After determining the first probability value, the construction external failure related factor information can be further analyzed through the construction external failure negation factor (i.e., the exclusion factor analysis rule), the first probability value can be updated, and the second probability value that the fault is a construction external failure can be determined.

[0128] 9. The final probability value of the fault being a construction external failure is determined by the distributed probability of the construction external failure determined by the distributed waveform (i.e., the third probability value) and the second probability value.

[0129] In this embodiment, by acquiring distributed fault information when detecting protection action information, and integrating distributed waveform identification result information and lightning monitoring information, it is determined whether it is a non-lightning fault. If the fault type belongs to a non-lightning fault, the distributed / recorded waveform characteristics are analyzed to determine whether the fault belongs to a metallic fault, and whether the fault belongs to a non-natural disaster fault through early warning and monitored meteorological disaster information. If the fault type belongs to a metallic fault and a non-natural disaster fault, the construction external failure associated factor information corresponding to the fault is determined through the fault information and the second environmental monitoring information. It is determined whether the construction external failure associated factor information meets the key factor judgment rules; if it does, the fault is determined to be a construction external failure fault. If it does not meet the requirements, based on the construction external failure regularity factor and the manually entered fault factor, the first probability value of the fault being a construction external failure fault is determined through the coupling factor judgment rules. After determining the first probability value, the construction external failure associated factor information can be further judged through the construction external failure negation factor, and the first probability value is updated to determine the second probability value of the fault being a construction external failure fault. The distributed probability of construction external failure determined by the distributed waveform and the second probability value are used to determine the final probability value of the fault being a construction external failure. By accurately analyzing and determining the probability of a construction external failure, the accuracy and reliability of line fault diagnosis are effectively improved.

[0130] Embodiment 3

[0131] Figure 4 This is a schematic diagram of the structure of a construction external failure diagnosis device provided by the third embodiment of the present invention. Figure 4 As shown, the device comprises:

[0132] An acquisition module 310 is used to acquire fault information, identify the fault information through a fault identification model to obtain identification result information, and determine the fault type based on the identification result information and the first environment monitoring information;

[0133] The judgment module 320 is used to determine whether the fault type is a metallic fault by performing feature analysis on the fault waveform information in the fault information when the fault type is a non-lightning fault, and determine whether the fault type is a non-natural disaster fault by using the second environment monitoring information;

[0134] The determination module 330 is used to determine the probability that the fault is a construction external failure based on the fault information and the second environmental monitoring information and according to the preset construction external failure failure correlation factor analysis rules when the fault type is a metallic fault or a non-natural disaster fault.

[0135] Optionally, the acquisition module 310 is specifically configured to:

[0136] When protection action information is detected, obtaining the fault information;

[0137] Based on the fault identification model, time-frequency domain features of the fault waveform information in the fault information are extracted and analyzed to determine the identification result information.

[0138] Optionally, the device further includes: a training module, wherein the training module is specifically used for:

[0139] Construct an initial fault identification model based on the SMOTE algorithm and deep forest algorithm;

[0140] The initial fault identification model is trained by using a historical fault data set to obtain the trained fault identification model.

[0141] Optionally, the construction external failure correlation factor includes at least one of the following: visualization information, fault phase information, fault time and meteorological information, wherein the visualization information and the fault time are determined by the fault information, the fault phase information is determined by the fault waveform information, and the meteorological information is determined by the second environment monitoring information;

[0142] The preset construction external failure correlation factor analysis rules include: key factor analysis rules, coupling factor analysis rules and exclusion factor analysis rules.

[0143] Optionally, the determination module 330 is specifically configured to:

[0144] Based on the fault information and the second environment monitoring information, determining the construction external damage correlation factor information corresponding to the fault;

[0145] Determine whether the construction external failure correlation factor information complies with the key factor analysis rules;

[0146] If it is true, it is determined that the fault is a construction failure;

[0147] If not, based on the construction external failure correlation factor information and through the coupling factor analysis rule, determine the first probability value of the fault being a construction external failure fault;

[0148] Based on the construction external failure associated factor information, the first probability value is updated through the exclusion factor analysis rule to determine a second probability value that the fault is a construction external failure fault;

[0149] A probability value that the fault is a construction failure fault is determined based on the second probability value.

[0150] Optionally, the determination module 330 is specifically configured to:

[0151] Based on the fault identification model, obtaining a third probability value that the fault is a construction failure;

[0152] Based on the third probability value and the second probability value, a probability value that the fault is a construction failure is determined.

[0153] The construction external failure fault diagnosis device provided in the embodiment of the present invention can execute the construction external failure fault diagnosis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0154] Embodiment 4

[0155] Figure 5 A schematic diagram of an electronic device that can be used to implement an embodiment of the present invention is shown. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0156] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by the at least one processor 11, and the computer program is executed by the at least one processor 11 so that the at least one processor 11 can execute the method provided by the present invention.

[0157] The processor 11 can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 12 or a computer program loaded from a storage unit 18 into a random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0158] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0159] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a construction external failure fault diagnosis method.

[0160] In some embodiments, the construction external failure fault diagnosis method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the construction external failure fault diagnosis method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the construction external failure fault diagnosis method in any other appropriate manner (for example, by means of firmware).

[0161] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard parts (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0162] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0163] In the context of the present invention, a computer-readable storage medium stores computer instructions, which are used to implement the construction external failure fault diagnosis method provided by the present invention when the processor executes. The computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0164] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD monitor)) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user may provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0165] The systems and techniques described herein may be implemented in a computing system that includes a backend component (e.g., as a data server), or a computing system that includes a middleware component (e.g., an application server), or a computing system that includes a frontend component (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include: a Local Area Network (LAN), a Wide Area Network (WAN), a blockchain network, and the Internet.

[0166] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services.

[0167] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0168] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A construction external failure diagnosis method, characterized in that: include: Acquire fault information, identify the fault information through a fault identification model to obtain identification result information, and determine the fault type based on the identification result information and the first environment monitoring information; When the fault type belongs to a non-lightning fault, determining whether the fault type belongs to a metallic fault by performing feature analysis on the fault waveform information in the fault information, and determining whether the fault type belongs to a non-natural disaster fault by using the second environmental monitoring information; When the fault type is a metallic fault or a non-natural disaster fault, the probability that the fault is a construction external failure is determined based on the fault information and the second environmental monitoring information and according to the preset construction external failure fault correlation factor analysis rules.

2. The method according to claim 1, characterized in that The acquiring of fault information and identifying the fault information by a fault identification model to obtain identification result information includes: When protection action information is detected, obtaining the fault information; Based on the fault identification model, time-frequency domain features of the fault waveform information in the fault information are extracted and analyzed to determine the identification result information.

3. The method according to claim 1, characterized in that Also includes: Construct an initial fault identification model based on the SMOTE algorithm and deep forest algorithm; The initial fault identification model is trained by using a historical fault data set to obtain the trained fault identification model.

4. The method according to claim 1, characterized in that The construction external failure correlation factor includes at least one of the following: visualization information, fault phase information, fault time and meteorological information, wherein the visualization information and the fault time are determined by the fault information, the fault phase information is determined by the fault waveform information, and the meteorological information is determined by the second environment monitoring information; The preset construction external failure correlation factor analysis rules include: key factor analysis rules, coupling factor analysis rules and exclusion factor analysis rules.

5. The method according to claim 4, characterized in that Based on the fault information and the second environmental monitoring information, according to the preset construction external failure fault correlation factor analysis rule, the probability of construction external failure fault diagnosis is determined, including: Based on the fault information and the second environment monitoring information, determining the construction external damage correlation factor information corresponding to the fault; Determine whether the construction external failure correlation factor information complies with the key factor analysis rules; If it is true, it is determined that the fault is a construction failure; If not, based on the construction external failure correlation factor information and through the coupling factor analysis rule, determine the first probability value of the fault being a construction external failure fault; Based on the construction external failure associated factor information, the first probability value is updated through the exclusion factor analysis rule to determine a second probability value that the fault is a construction external failure fault; A probability value that the fault is a construction failure fault is determined based on the second probability value.

6. The method according to claim 5, characterized in that The determining, based on the second probability value, a probability value of the fault being a construction failure fault includes: Based on the fault identification model, obtaining a third probability value that the fault is a construction failure; Based on the third probability value and the second probability value, a probability value that the fault is a construction failure is determined.

7. A construction external failure diagnosis device, characterized in that: include: An acquisition module, used to acquire fault information, identify the fault information through a fault identification model to obtain identification result information, and determine the fault type based on the identification result information and the first environment monitoring information; A judgment module, used for determining whether the fault type is a metallic fault by performing feature analysis on the fault waveform information in the fault information when the fault type is a non-lightning fault, and determining whether the fault type is a non-natural disaster fault by using the second environmental monitoring information; The determination module is used to determine the probability that the fault is a construction external failure based on the fault information and the second environmental monitoring information and according to the preset construction external failure failure correlation factor analysis rules when the fault type is a metallic fault or a non-natural disaster fault.

8. The device according to claim 7, characterized in that The determination module is specifically used for: Based on the fault information and the second environment monitoring information, determining the construction external damage correlation factor information corresponding to the fault; Determine whether the construction external failure correlation factor information complies with the key factor analysis rules; If it is true, it is determined that the fault is a construction failure; If not, based on the construction external failure correlation factor information, a first probability value of the fault being a construction external failure is determined through a coupling factor analysis rule; Based on the construction external failure associated factor information, the first probability value is updated by excluding factor analysis rules to determine a second probability value that the fault is a construction external failure fault; A probability value that the fault is a construction failure fault is determined based on the second probability value.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the construction external failure fault diagnosis method described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the construction external failure fault diagnosis method described in any one of claims 1-6 when executed.

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